Activated Clec4nhi Neutrophils Aggravate Lung Injury in an Endothelial IGFBP7 Dependent Manner.

The role of endothelial cells in Acute lung injury (ALI) has been widely elaborated, but little is known about the role of different subtypes of endothelial cells in ALI. ALI models were established by lipopolysaccharide. Single-cell RNA sequencing was used to identify differential molecules in endothelial subtypes and heterogeneity of lung immune cells. Specific antibodies were used to block IGFBP7 protein, and AAVshIGP7 was used to specifically knock down IGFBP7. Here, we found that Insulin-like growth factor binding protein 7 (IGFBP7) was the most differentially expressed molecule in diverse subsets of endothelial cells and that IGFBP7 was strongly associated with inflammatory responses. Elevated IGFBP7 significantly exacerbated barrier dysfunction in ALI, whereas blockade of IGFBP7 partially reversed barrier damage. General capillary cells (gCap) are the primary source of elevated serum IGFBP7 after ALI. Using single-cell RNA sequencing, we identified significantly increased Clec4nhi neutrophils in ALI mice, whereas IGFBP7 knockdown significantly reduced infiltration of Clec4nhi cells and mitigated barrier dysfunction in ALI. In addition, we found that IGFBP7 activated the NFκB signaling axis by promoting phosphorylation and ubiquitination degradation of F-box/WD repeat-containing protein 2 (FBXW2), thereby exacerbating barrier dysfunction in ALI. Taken together, our data indicate that blockade of serum IGFBP7 or IGFBP7 depletion in gCap reversed barrier damage in ALI. Therefore, targeting the IGFBP7 depletion could be a novel strategy for treating ALI.

Chronic sleep deprivation impairs retinal circadian transcriptome and visual function.

Sleep loss is common in modern society and is increasingly associated with eye diseases. However, the precise effects of sleep loss on retinal structure and function, particularly on the retinal circadian system, remain largely unexplored. This study investigates these effects using a chronic sleep deprivation (CSD) model in mice. Our investigation reveals that CSD significantly alters the retinal circadian transcriptome, leading to remarkable changes in the temporal patterns of enriched pathways. This perturbation extends to metabolic and immune-related transcriptomes, coupled with an accumulation of reactive oxygen species in the retina. Notably, CSD rhythmically affects the thickness of the ganglion cell complex, along with diurnal shifts in microglial migration and morphology within the retina. Most critically, we observe a marked decrease in both scotopic and photopic retinal function under CSD conditions. These findings underscore the broad impact of sleep deprivation on retinal health, highlighting its role in altering circadian gene expression, metabolism, immune response, and structural integrity. Our study provides new insights into the broader impact of sleep loss on retinal health.

Dynamics of the surface carbonate system in oil fields with a high concentration of wells on the northwestern South China Sea shelf.

Oil exploitation may pose adverse effects on marine ecosystems, but its impacts on surface carbonate dynamics remain unknown. In a carbonate system with low air-sea ∆pCO2, such as the South China Sea (SCS), human activities may affect the pCO2 distribution patterns and potentially alter CO2 sink or source at the surface. This study investigates the surface carbonate system in two oil fields, namely the Wenchang Oil Feld and Enping Oil Feld, located on the northwestern SCS (NWSCS) shelf. In Enping Oil Field, although there is a slight increase in surface pCO2 due to probable total alkalinity (TA) consumption from CaCO3 precipitation, strong biological production makes the plume water a strong CO2 sink. Similarly, the biological processes dominated the pCO2 variability in Wenchang Oil Feld, exhibiting high values in its central area. In NWSCS, the influence of shelf water was observed during both cruises. And the pCO2 drawdown caused by the decreased sea surface temperature (SST) and CO2 outgassing outweighed their increases via enhanced vertical mixing, leading to a pCO2 drawdown from September to October within this water mass. More importantly, there were no significant disparities observed in carbonate parameters at stations along transects with and without wells, and the observed parameter values in this study fell within the range reported previously on the nSCS shelf with similar controlling processes. Thus the impact of oil exploitation on carbonate dynamics is negligible, and the characteristics of the carbonate system in oil field are primarily governed by natural processes such as the mixing of plume water and basin water, CaCO3 precipitation and the changes in SST. The provided data establish a crucial baseline for detecting future alterations in carbonate chemistry within oil fields, and the rapid fluctuations in sea surface pCO2 highlight the need for higher spatiotemporal resolution observation.

Few-Shot Object Detection With Self-Supervising and Cooperative Classifier.

Few-shot object detection (FSOD), which detects novel objects with only a few training instances, has recently attracted more attention. Previous works focus on making the most use of label information of objects. Still, they fail to consider the structural and semantic information of the image itself and solve the misclassification between data-abundant base classes and data-scarce novel classes efficiently. In this article, we propose FSOD with Self-Supervising and Cooperative Classifier ( [Formula: see text]) approach to deal with those concerns. Specifically, we analyze the underlying performance degradation of novel classes in FSOD and discover that false-positive samples are the main reason. By looking into these false-positive samples, we further notice that misclassifying novel classes as base classes are the main cause. Thus, we introduce double RoI heads into the existing Fast-RCNN to learn more specific features for novel classes. We also consider using self-supervised learning (SSL) to learn more structural and semantic information. Finally, we propose a cooperative classifier (CC) with the base-novel regularization to maximize the interclass variance between base and novel classes. In the experiment, [Formula: see text] outperforms all the latest baselines in most cases on PASCAL VOC and COCO.

Single-session intervention on growth mindset on negative emotions for university student mental health (U-SIGMA): a protocol of two-armed randomized controlled trial.

BACKGROUND: The university years are a developmentally crucial phase and a peak period for the onset of mental disorders. The beliefs about the changeability of negative emotion may play an important role in help-seeking. The brief digital growth mindset intervention is potentially scalable and acceptable to enhance adaptive coping and help-seeking for mental health needs in university students. We adapted the Single-session Intervention on Growth Mindset for adolescents (SIGMA) to be applied in university students (U-SIGMA). This protocol introduces a two-armed waitlist randomized controlled trial study to examine the effectiveness and acceptability of U-SIGMA in promoting help-seeking among university students in the Greater Bay Area. METHODS: University students (N = 250, ages 18-25) from universities in the Greater Bay Area will be randomized to either the brief digital growth mindset intervention group or the waitlist control group. Participants will report on the mindsets of negative emotions, perceived control over anxiety, attitude toward help-seeking, physical activity, hopelessness, psychological well-being, depression, anxiety, and perceived stress at baseline and the 2-week and 8-week follow-ups through web-based surveys. A 30-min digital intervention will be implemented in the intervention group, with a pre- and post-intervention survey collecting intervention feedback, while the control group will receive the link for intervention after 8 weeks. DISCUSSION: This protocol introduces the implementation plan of U-SIMGA in multi-cities of the Greater Bay Area. The findings are expected to help provide pioneer evidence for the effectiveness and acceptability of the brief digital intervention for university students in the Chinese context and beyond and contribute to the development of accessible and effective prevention and early intervention for university students' mental health. TRIAL REGISTRATION: HKU Clinical Trials Registry: HKUCTR-3012; Registered 14 April 2023.  http://www.hkuctr.com/Study/Show/7a3ffbc0e03f4d1eac0525450fc5187e .

Peak-tracking BOTDA with dynamic ternary search.

We propose a peak-tracking BOTDA (PT-BOTDA) equipped with an efficient dynamic Brillouin frequency shift (BFS) searching scheme based on ternary search. The proposed scheme establishes a feedback loop between the selected frequency and the corresponding Brillouin gain to reduce the required number of scanning frequencies in one measurement. We also demonstrate the performance evaluation of the proposed scheme under scenarios with different searching granularities and dynamic sensing ranges. Experimental results indicate that in all situations, the proposed PT-BOTDA can achieve at least 85% and 97% reduction in the number of scanning frequencies for 1-MHz and 0.1-MHz frequency steps, respectively, with a 3-meter spatial resolution, while maintaining a convincing BFS searching accuracy under sufficient SNR condition using a smaller searching interval.

High-order moment closure models with random batch method for efficient computation of multiscale turbulent systems.

We propose a high-order stochastic-statistical moment closure model for efficient ensemble prediction of leading-order statistical moments and probability density functions in multiscale complex turbulent systems. The statistical moment equations are closed by a precise calibration of the high-order feedbacks using ensemble solutions of the consistent stochastic equations, suitable for modeling complex phenomena including non-Gaussian statistics and extreme events. To address challenges associated with closely coupled spatiotemporal scales in turbulent states and expensive large ensemble simulation for high-dimensional systems, we introduce efficient computational strategies using the random batch method (RBM). This approach significantly reduces the required ensemble size while accurately capturing essential high-order structures. Only a small batch of small-scale fluctuation modes is used for each time update of the samples, and exact convergence to the full model statistics is ensured through frequent resampling of the batches during time evolution. Furthermore, we develop a reduced-order model to handle systems with really high dimensions by linking the large number of small-scale fluctuation modes to ensemble samples of dominant leading modes. The effectiveness of the proposed models is validated by numerical experiments on the one-layer and two-layer Lorenz '96 systems, which exhibit representative chaotic features and various statistical regimes. The full and reduced-order RBM models demonstrate uniformly high skill in capturing the time evolution of crucial leading-order statistics, non-Gaussian probability distributions, while achieving significantly lower computational cost compared to direct Monte-Carlo approaches. The models provide effective tools for a wide range of real-world applications in prediction, uncertainty quantification, and data assimilation.

Phylogeography and Genetic Structure of the Bush Cricket Decma fissa (Orthoptera, Tettigoniidae) in Southern China.

Decma fissa is the most widely distributed species of the genus Decma occuring in southern China. This study presents the first phylogeographic work of D. fissa based on COI, Cytb and ITS sequence. We examined genetic diversity with ITS and mitochondrial sequence respectively, and phylogenetic work was based on the mitochondrial data. A high-level genetic diversity was revealed based on mitochondrial data but a low-level diversity was shown with ITS sequence. For the mitochondrial data, divergence time analysis displayed five lineages. Based on the Mantel test, geographic and genetic distances among D. fissa populations revealed a significant positive correlation. Bayesian skyline plot (BSP) analyses implied that none of three major lineages of D. fissa was seemingly affected by the last glacial maximum (LGM, 0.015-0.025 Mya). Ecological niche modeling was used to predict the distribution of D. fissa in four periods (LGM, Mid-Holocene, current and 2070) in China. Analysis of the ancestral area reconstruction indicated that D. fissa occurred in the South China area.

Mechanisms of Extraorbital Lacrimal Gland Aging in Mice: An Integrative Analysis of the Temporal Transcriptome.

Purpose: This study used high-throughput RNA sequencing (RNA-Seq) and bioinformatics analysis to investigate the altered transcriptome profile of aging lacrimal glands in mice that occurs over the course of a 24-hour cycle. Methods: Male C57BL/6J mice aged 12 weeks (young) and 20 months (aging) were housed in a pathogen-free setting with a 12-hour light/12-hour dark cycle. Throughout a 24-hour cycle, mouse extraorbital lacrimal glands (ELGs) were collected at eight time points at three-hour intervals. To prepare for the high-throughput RNA-Seq, whole mRNA was extracted. Differentially expressed genes (DEGs) in the young and aging groups were subjected to bioinformatic analysis based on diurnal patterns. Furthermore, the cell populations in which significant DEGs express and signaling pathways occur were validated at the single-cell RNA sequencing (scRNA-seq) level. Results: The total transcriptome composition was significantly altered in aging ELGs compared with that in young mouse ELGs at eight time points during the 24-hour cycle, with 864 upregulated and 228 downregulated DEGs, which were primarily enriched in inflammatory pathways. Further comparative analysis of the point-to-point transcriptome revealed that aging ELGs underwent alterations in the temporal transcriptome profile in several pathways, including the inflammation-related, metabolism-related, mitochondrial bioenergetic function-associated, synaptome neural activity-associated, cell processes-associated, DNA processing-associated and fibrosis-associated pathways. Most of these pathways occurred separately in distinct cell populations. Conclusions: Transcriptome profiles of aging lacrimal glands undergo considerable diurnal time-dependent changes; this finding offers a comprehensive source of information to better understand the pathophysiology of lacrimal gland aging and its underlying mechanisms.

Mechanisms of dihydromyricetin against hepatocellular carcinoma elucidated by network pharmacology combined with experimental validation.

CONTEXT: Dihydromyricetin (DMY) is extracted from vine tea, a traditional Chinese herbal medicine with anti-cancer, liver protection, and cholesterol-lowering effects. OBJECTIVE: This study investigated the mechanism of DMY against hepatocellular carcinoma (HCC). MATERIALS AND METHODS: Potential DMY, HCC, and cholesterol targets were collected from relevant databases. PPI networks were created by STRING. Then, the hub genes of co-targets, screened using CytoHubba. GO and KEGG pathway enrichment, were performed by Metascape. Based on the above results, a series of in vitro experiments were conducted by using 40-160 µM DMY for 24 h, including transwell migration/invasion assay, western blotting, and Bodipy stain assay. RESULTS: Network pharmacology identified 98 common targets and 10 hub genes of DMY, HCC, and cholesterol, and revealed that the anti-HCC effect of DMY may be related to the positive regulation of lipid rafts. Further experiments confirmed that DMY inhibits the proliferation, migration, and invasion of HCC cells and reduces their cholesterol levels in vitro. The IC50 is 894.4, 814.4, 467.8, 1,878.8, 151.8, and 156.9 µM for 97H, Hep3B, Sk-Hep1, SMMC-7721, HepG2, and Huh7 cells, respectively. In addition, DMY downregulates the expression of lipid raft markers (CAV1, FLOT1), as well as EGFR, PI3K, Akt, STAT3, and Erk. DISCUSSION AND CONCLUSION: The present study reveals that DMY suppresses EGFR and its downstream pathways by reducing cholesterol to disrupt lipid rafts, thereby inhibiting HCC, which provides a promising candidate drug with low toxicity for the treatment of HCC.

Concentrations and controls of dissolved inorganic carbon in Arctic summer sea ice and adjacent surface seawaters.

The carbonate chemistry of sea ice plays a critical role in global ocean carbon cycles, particularly in polar regions which are subject to significant climate change-induced sea ice variation. However, less is known about the interaction of carbonate system between sea ice and its adjacent seawaters due to sparse sampling and disparities in reported results. Here we provide an insight into this issue by collecting and measuring dissolved inorganic carbon (DIC) and associated environmental parameters in Arctic sea ice during a cruise in the summer of 2014. Our observations show that DIC in Arctic summer sea ice has a mean concentration of 463.3 ± 213.0 µmol/kg and appears to be controlled mainly by the fraction of brine water in the ice. The low Chl a and nutrients content in sea ice indicate minor contribution of biological uptake to sea-ice DIC in the western Arctic Ocean. The DIC concentration in surface water (<100 m depth) decreased from a mean of 2108.3 ± 45.4 µmol/kg in 1994 to a mean of 2052.4 ± 98.6 µmol/kg in 2014, due to the enhanced sea ice melting that dilutes the DIC concentrations of surrounding seawaters.

Long-term high fructose intake promotes lacrimal gland dysfunction by inducing gut dysbiosis in mice.

The lacrimal gland is essential for maintaining ocular surface health through the secretion of the aqueous layer of the tear film. It is therefore important to explore the intrinsic and extrinsic factors that affect the structure and function of the lacrimal gland and the mechanisms underlying them. With the prevalence of Westernized diets characterized by high sugar and fat content, the susceptibility to many diseases, including ocular diseases, is increased by inducing dysbiosis of the gut microbiome. Here, we found that the composition, abundance, and diversity of the gut microbiome was significantly altered in mice by drinking 15% high fructose water for one month, as determined by 16S rRNA sequencing. This was accompanied by a significant increase in lipid deposition and inflammatory cell infiltration in the extraorbital lacrimal glands (ELGs) of mice. Transcriptome analysis based on bulk RNA-sequencing revealed abnormal activation of some of several metabolic and immune-related pathways. In addition, the secretory response to stimulation with the cholinergic receptor agonist pilocarpine was significantly reduced. However, when the composition and diversity of the gut microbiome of high fructose intake (HFI)-treated mice were improved by transplanting feces from normal young healthy mice, the pathological alterations in ELG structure, inflammatory cell infiltration, secretory function and transcriptome analysis described above were significantly reversed compared to age-matched control mice. In conclusion, our data suggest that prolonged HFI may cause pathological damage to the structure and function of the ELG through the induction of gut dysbiosis. Restoration of intestinal dysbiosis in HFI-treated mice by fecal transplantation has a potential role in ameliorating these pathological impairments.

Biogeochemical and physical controls on ammonium accumulation on the Chukchi shelf, western Arctic Ocean.

Spatial variability of ammonium concentrations along repeat transects were examined on the Chukchi shelf during 2012-2018. Two distinct near-bottom high ammonium pools (>1 µmol/kg) near 67.5°N and 72.5°N of the transects were identified in all years. The accumulation of ammonium in the regions is driven primarily by a combination of biogeochemical processes (e.g., dynamic bacterial remineralization of organic matter) and physical controls (e.g., strong density-contrast barrier limits upward mixing of ammonium). The ammonium pool on the shelf may became larger in the expectation of the stronger bacterial remineralization following elevate primary production, and may have potential impact on the structure and productivity of ecosystem on the Chukchi shelf.

High-fat intake reshapes the circadian transcriptome profile and metabolism in murine meibomian glands.

Background: Nutritional and food components reshape the peripheral clock and metabolism. However, whether food challenges affect the circadian clock and metabolism of meibomian glands (MGs) has not been fully explored. This study was designed to analyze alterations in the rhythmic transcriptome and metabolism of MGs of murine fed a balanced diet or a high-fat diet (HFD). Methods: Male C57BL/6J mice were maintained on a 12/12 h light/dark cycle and fed ad libitum on normal chow (NC) or HFD for 4 weeks. MGs were collected from sacrificed animals at 3-h intervals throughout a 24-h circadian cycle. The circadian transcriptome of MGs was analyzed via bioinformatics approaches using high-throughput RNA sequencing (RNA-seq). In addition, circadian oscillations of lipid components in MGs were analyzed. Results: Meibomian glands displayed robust transcriptome rhythmicity. HFD feeding significantly altered the circadian transcriptome profile of MGs-including composition and phase-and spatiotemporally affected the enriched signaling pathways. In addition, HFD feeding significantly altered the normal rhythmic oscillations of lipid components in MGs. Conclusion: Our data show that HFD significantly affects MGs' rhythmicity, which reveals a high sensitivity of MGs' clocks to lipid composition in food.

Screening marker genes of type 2 diabetes mellitus in mouse lacrimal gland by LASSO regression.

Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance and a relative deficiency of insulin. This study aims to screen T2DM-related maker genes in the mouse extraorbital lacrimal gland (ELG) by LASSO regression.C57BLKS/J strain with leptin db/db homozygous mice (T2DM, n = 20) and wild-type mice (WT, n = 20) were used to collect data. The ELGs were collected for RNA sequencing. LASSO regression was conducted to screen marker genes with the training set. Five genes were selected from 689 differentially expressed genes by LASSO regression, including Synm, Elovl6, Glcci1, Tnks and Ptprt. Expression of Synm was downregulated in ELGs of T2DM mice. Elovl6, Glcci1, Tnks, and Ptprt were upregulated in T2DM mice. Area under receiver operating curve of the LASSO model was 1.000(1.000-1.000) and 0.980(0.929-1.000) in the training set and the test set, respectively. The C-index and the robust C-index of the LASSO model were 1.000 and 0.999, respectively, in the training set, and 1.000 and 0.978, respectively, in the test set. In the lacrimal gland of db/db mice, Synm, Elovl6, Glcci1, Tnks and Ptprt can be used as marker genes of T2DM. Abnormal expression of marker genes is related to lacrimal gland atrophy and dry eye in mice.

A random batch method for efficient ensemble forecasts of multiscale turbulent systems.

A new efficient ensemble prediction strategy is developed for a multiscale turbulent model framework with emphasis on the nonlinear interactions between large and small-scale variables. The high computational cost in running large ensemble simulations of high-dimensional equations is effectively avoided by adopting a random batch decomposition of the wide spectrum of the fluctuation states, which is a characteristic feature of the multiscale turbulent systems. The time update of each ensemble sample is then only subject to a small portion of the small-scale fluctuation modes in one batch, while the true model dynamics with multiscale coupling is respected by frequent random resampling of the batches at each time updating step. We investigate both theoretical and numerical properties of the proposed method. First, the convergence of statistical errors in the random batch model approximation is shown rigorously independent of the sample size and full dimension of the system. Next, the forecast skill of the computational algorithm is tested on two representative models of turbulent flows exhibiting many key statistical phenomena with a direct link to realistic turbulent systems. The random batch method displays robust performance in capturing a series of crucial statistical features with general interests, including highly non-Gaussian fat-tailed probability distributions and intermittent bursts of instability, while requires a much lower computational cost than the direct ensemble approach. The efficient random batch method also facilitates the development of new strategies in uncertainty quantification and data assimilation for a wide variety of general complex turbulent systems in science and engineering.